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Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS's soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R 2 and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m 3 s -1 and 0.81, 2.297 m 3 s -1 , respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R 2 and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m 3 s -1 , respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting.

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This study introduces two artificial neural network (ANN)-based methodologies to predict hourly water levels (WLs) in wetlands characterized by water tables at or near the surface that respond rapidly to precipitation. The first method makes use of hourly precipitation data and WL data from nearby sites. The second method is a combination of ANN, recursive digital filter, and recession curve method and does not require any nearby site. The proposed methods were tested at two headwater wetlands in coastal Alabama. Site 17 had two nearby sites whose WLs were highly correlated with Site 17's. The root-mean-square error and Nash-Sutcliffe efficiencies were 2.9 cm and 0.98, respectively, when the first method was applied to Site 17. The second method was tested at Site 32. For this, the WL time series was separated into quick-and slow-response components. A combination of ANN and base-flow separation methods proved to be very efficient for WL prediction at this site, especially when the duration of quick-response components of individual events was less than 6 h. The proposed methodologies, therefore, proved useful in predicting WLs in wetlands dominated by both surface water and groundwater. individual papers. This paper is part of the Journal of Hydrologic Engineering, © ASCE, ISSN 1084-0699/D4015003 (11)/$25.00. © ASCE D4015003-1 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-5 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-8 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-9 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-11 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15.

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